SEARCH · Engineering Papers

Results for “big”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

SourceResearch connectionFrom yearThrough year

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 1,135 records · Page 63

All sources NASA NTRS DOE OSTI

DOE OSTI2021

Using machine learning and artificial intelligence to improve model-data integrated earth system model predictions of water and carbon cycle extremes

The research proposed here focuses on improving the predictive power of the land component of earth system models (ESMs) using (1) model-data fusion enabled by machine learning (ML) and artificial intelligence (AI), (2) predictive modeling through the combination of ML, AI, and big-data (comprising both model output and observations), and (3) insight of ESM structure and process mechanisms gleaned from complex data using ML and AI.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2020

2018 Report on Rare Plant Monitoring and Restoration at the Lawrence Livermore National Laboratory’s Experimental Test Site (Site 300)

This document meets the reporting requirements of LLNL’s California Endangered Species Act Scientific, Educational, or Management Permit (No. 2081 (a)-15-005-RP) for 2018. Section A describes monitoring and field activities conducted by LLNL biologists at the Site 300 native and experimental populations and the Carnegie Canyon native population of the federally-endangered plant, Amsinckia grandiflora (the large-flowered fiddleneck), between January 2018 and December 2018. In addition, monitoring and field activities conducted by LLNL biologists at the Site 300 native populations of the rare plants Eschscholzia rhombipetala (the diamond-petaled California poppy) and Blepharizonia plumosa (the big tarplant) between January 2018 and December 2018 are described in Sections B and C, respectively. Both plants have a California Rare Plant Rank (CRPR) of 1B, which includes species that are rare or endangered throughout their range. California macrophylla (the round-leaved filaree), formerly had a CRPR of 1B, but was downgraded to CBR (Considered But Rejected) in December 2017, as the species was “too common statewide: counties that contain small, localized populations under severe threat should track C. macrophylla as a species of local concern” (California Native Plant Society, 2018). As C. macrophylla is widespread at Site 300 (Carlsen and Paterson, 2018), 2018 will be the final year of monitoring C. macrophylla at Site 300, the results of which are described in Section D. Monitoring of the Navarretia nigelliformis ssp. radians (adobe navarretia), a CRPR 1B species, is planned, and results will be included in future reports.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

Evaluating Offshore Infrastructure Integrity

Drilling in the offshore environment involves a complex network of infrastructure including pipelines, platforms, rigs, subsea installations, ports, and terminals. Government and industry partners have developed this network over many decades and it remains a critical part of the United States (U.S.) energy portfolio. Many of the major components of this system have been designed with a 20- to 30-year lifespan, yet consistent and growing energy demands support the need to extend the design life of existing infrastructure or repurpose it for secondary needs (i.e. enhanced oil recovery, carbon storage, and new wells). As a result, a growing portion of the offshore infrastructure in the U.S. is approaching or has exceeded its original design life. A critical step in ensuring the continued safe and effective operation of offshore infrastructure is developing a comprehensive understanding of the state of offshore infrastructure and the factors that effect it. The purpose of this project is to assess the current state of existing infrastructure and identify the factors involved in infrastructure degradation through the development and application of big data analytics, machine learning, and advanced spatio-temporal analysis. The project leverages existing data at NETL and combines it with new information on offshore oil and gas structures and the ambient offshore environment in an effort to identify patterns associated with infrastructure integrity. Building on the identified trends and patterns, this project incorporates exploratory analytics and spatial analysis tools in conjunction with machine learning and statistical models to characterize the condition of existing platforms in the offshore environment and predict their risk of failure.

02 PETROLEUM↗

DOE OSTI2021

Notes from the GEM Chair

I hope this letter finds you all safe and healthy. Due to the continuing uncertainties with the COVID-19 pandemic, the GEM Steering Committee (SC) decided, similarly to last year, to once again hold a fully virtual workshop. The GEM 2021 summer workshop will occur from July 26 to July 30, with Sunday July 25 as the Student Day. We will follow the original format of GEM, with plenary sessions in the morning, Focus Group (FG) breakout sessions in the afternoon, and poster sessions in the evening. We encourage you to take this opportunity to share your research, technical efforts, and ideas with the GEM community. Although we realize that the big celebration will happen once we are able to meet in person again, this year is the 30th anniversary of the first GEM meeting and we envision a program that celebrates this significant milestone!

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2021

Evolving Architectures and Considerations to address Distributed Energy Resources and Non-Wired Alternatives

The electric grid is in the beginning stage of a transformation, driven by a combination of shutdowns of coal-fired plants, commissioning of new natural-gas plants, and tremendous growth in energy supply from renewables such as wind, and solar. As utilities navigate this transformation, their progress is supported by advances in Operational Technologies (OT), and Informational Technologies (IT), such as automation, smart inverters, cloud computing, mobile computing, machine learning, big data analytics, which have the potential to enable advanced capabilities more efficiently and at a lower cost. This white paper focuses on the architectural considerations that will allow the industry to transition in a planned manner. It introduces and formalizes two architectural constructs –the data bus and the control bus. The data bus is responsible for carrying all non-operational models and information necessary to drive utility decisions. In contrast, the control bus is responsible for carrying all operational data and control actions taken at the local level, centralized level, or other levels in-between. The paper reviews those architectural considerations, their requirements and how they will evolve. This paper intends to provide a context for vendors, utilities, and their service providers to review and understand the changes that are coming and get ready for them.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2021

Science and Technology Review (May 2021)

Lawrence Livermore has been on the forefront of cancer research for over 60 years. Early interest in cancer statistics stemmed from the nature of work, particularly how radiation affects humans. The Department of Energy funded research to investigate the effects of radiation on workers with long term exposure. This research quickly morphed into a wider breadth of cancer research topics, including the use of advanced computational models to investigate mutations in genes. Livermore is regarded as a leader in cancer research, from the Human Genome Center to its participation in the National Cancer Institute’s “Moonshot” project. The highly interdisciplinary Laboratory unites research in one more example: bringing together cancer biology, 3D printing, high-performance computing, big data, and materials science to address this pressing medical challenge.

36 MATERIALS SCIENCE↗

DOE OSTI2021

Interpolation data transfer between the models before and after a partial drawdown leach in BH

It has been recognized that as cavern operations become more frequent due to oil sales, field conditions may arise which require a faster turnaround time of analysis to address potential cavern impacts. This letter describes attempts to implement a strategy of transferring an intermediate solution of a Big Hill (BH) geomechanical model from a previous finite element mesh with a specified cavern geometry, to a new mesh with a new cavern geometry created by leaching from an oil sale operation.

02 PETROLEUM↗

DOE OSTI2021

Mineral-Ground Micro-Fibrillated Cellulose Reinforcement for Polymer Compounds

Additive Manufacturing (AM) is quickly becoming a mainstream, energy efficient manufacturing technique for complex and custom components. ORNL has taken polymer AM into the next level with Big Area Additive Manufacturing (BAAM). However, we need to develop the strength of AM materials (specifically polymers) to satisfy the mechanical requirements of structural parts. Also, it is crucial that the developed process can easily be scaled up to have a significant impact in the manufacturing industry of US. Furthermore, renewable, bio-derived AM materials are desirable from a sustainability perspective. In the first phase of this project the mechanical properties of polymer AM components were significantly improved developing a micro fibrillated cellulose (MFC)-reinforced composite material system that is derived from biomass and compatible with BAAM. Unfortunately, the improvements were observed only with freeze-dried MFCs, and freeze-drying is a very slow process and is not industrially scalable. Furthermore, MFCs were not available in large quantities at low cost. However, FiberLean has reached 12,000 tons/year capacity (60,000 tons/year including the minerals). FiberLean offers MFCs at a scale and order of magnitude lower price than current market participants. With their recently developed novel (patent pending) drying process FiberLean can achieve over 50% solid content commercially. In the 2nd phase of the project, the original objective was to investigate this new novel drying process along with compounding with different formulations to optimize and adapt to large scale additive manufacturing. However, the compounded materials prepared via this approach were very brittle and demonstrated poor mechanical performance. Therefore, the project was focused more on a scalable chemical surface modification of MFCs to enable their drying via conventional oven process with minimal agglomeration and better dispersion in PLA polymer matrix. Fiberlean and ORNL have worked together in this project to develop mineral-ground microfibrillated cellulose (MFC) - reinforced additive manufacturing feedstock materials with significantly improved mechanical properties. Properly surface modified and dried Fiberlean cellulose microfibrils led to tensile strength increases up to ~40% and elastic modulus increases up to 70% with the addition of 30% MFC into PLA matrix. The work has led to a high impact journal article and a provisional patent. Opportunities for a follow up work to demonstrate the technology in large scale and application of it to AM applications are currently being searched.

36 MATERIALS SCIENCE↗

DOE OSTI2021

Research Software Engineering Efforts for DataFed: FY2021 Developments

DataFed is a scientific data management system for big data providing simple and uniform data access, organization, discovery, and sharing within and across scientific facilities - with the goals of enhancing productivity and scientific reproducibility. We hope to aid in communicating development contributions to DataFed over the last year and any planning we can provide for the future developments in the next year

42 ENGINEERING↗

DOE OSTI2021

Capturing a New State of Matter: Relativistic Electron-Positron Plasma (LDRD Final Report)

Relativistic electron-positron (pair) plasmas were a main component of the universe shortly after the Big Bang and are produced around many astrophysical objects, but laboratory pair plasmas have remained elusive. High-intensity lasers can drive dense relativistic positron jets; over the last ten years, we have optimized high-energy ultra-short-pulse laser-matter interaction experiments to make a large number (10 10 – 10 12 ) of positrons in a small volume (< 3 mm 3 ). However, these jets are transient (10 ps), making observations of collective effects difficult. The goal of 20-LW-021 was to trap laser-driven electron-positron jets using pulsed-power-driven high-magnetic-field solenoids, allowing longer confinement times and ultimately an observable laboratory relativistic pair plasma. The project has been remarkably successful: we have found that pulsed-power magnetic fields can effectively trap laser-driven charged particles and produce charge-neutral pair beams. The project thus far has resulted in six published journal articles. The success of these components has put a laboratory pair plasma almost within reach.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2021

Predictive Data-driven Platform for Subsurface Energy Production

Subsurface energy activities such as unconventional resource recovery, enhanced geothermal energy systems, and geologic carbon storage require fast and reliable methods to account for complex, multiphysical processes in heterogeneous fractured and porous media. Although reservoir simulation is considered the industry standard for simulating these subsurface systems with injection and/or extraction operations, reservoir simulation requires spatio-temporal “Big Data” into the simulation model, which is typically a major challenge during model development and computational phase. In this work, we developed and applied various deep neural network-based approaches to (1) process multiscale image segmentation, (2) generate ensemble members of drainage networks, flow channels, and porous media using deep convolutional generative adversarial network, (3) construct multiple hybrid neural networks such as convolutional LSTM and convolutional neural network-LSTM to develop fast and accurate reduced order models for shale gas extraction, and (4) physics-informed neural network and deep Q-learning for flow and energy production. We hypothesized that physicsbased machine learning/deep learning can overcome the shortcomings of traditional machine learning methods where data-driven models have faltered beyond the data and physical conditions used for training and validation. We improved and developed novel approaches to demonstrate that physics-based ML can allow us to incorporate physical constraints (e.g., scientific domain knowledge) into ML framework. Outcomes of this project will be readily applicable for many energy and national security problems that are particularly defined by multiscale features and network systems.

58 GEOSCIENCES↗

DOE OSTI2021

Facility Cybersecurity Framework Best Practices Version 2.0

Federal facilities are increasingly adopting automation and connecting to the Internet creating an energy-internet-of-things environment that converges operational technology (OT) and information technology (IT). Today's buildings increasingly weave together networked sensors and cyber and physical systems that enable data to be collected, aggregated, exchanged, stored and monetized in new ways. Building technological advances have created new energy technology, services, markets and value creation opportunities (e.g. transactive energy, two-way grid communications, machine learning, and increased use of renewable and distributed energy resources). But as larger data sets are being exchanged at faster speeds between an increasing number of OT systems, it becomes more difficult to protect the security of the data lifecycle and the physical equipment it interacts with. These challenges are especially difficult to overcome because the economic and environmental gain (interoperability, big data, social networks and ubiquitous information sharing) are driving these prominent trends in the digital age. Often cybersecurity is an afterthought.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2021

Facility Cybersecurity Framework Best Practices Version 2.0

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2022

International ACM Symposium on High Performance Parallel and Distributed Computing Conference for 2017, 2018, 2019, and 2020

The 28th ACM HPDC Conference was held in Phoenix, Arizona, June 24 and 28, 2019 (hpdc.org/2019), that was colocated with ACM FCRC 2019 (fcrc.acm.org). During the conference, Prof. Geoffrey Fox, Indiana University, was given the HPDC Achievement Award for 2019. Prof gave a keynote speech entitled “Perspectives on High-Performance Computing in a Big Data World. In addition, to the keynote speakers from HPDC and FCRC conferences, the conference organized successfully five workshops and one Ph.D. forum. The ACM FCRC had a total of 2700 attendees, and HPDC had a total of 120 attendees that included 32 students. We have used the DOE sponsorship to support the conference proceedings that acknowledge the DoE support and partially supported the travel to the HPDC PC meeting, Keynote speaker accommodation, best papers, presentation and poster award.

42 ENGINEERING↗

DOE OSTI2022

Analytics-at-scale of Sensor Data for Digital Monitoring in Nuclear Plants (3 rd Annual Report)

Nuclear power plants collect and store large volumes of heterogeneous data from various components and systems. With recent advances in machine learning (ML) techniques, these data can be leveraged to develop diagnostic and short-term forecasting models to better predict future equipment condition. Maintenance operations can then be planned in advance whenever degraded performance is predicted, thus resulting in fewer unplanned outages and the optimization of maintenance activities. This enables lower maintenance costs and improves the overall economics of nuclear power. This report primarily focuses on developing a short-term forecasting process that leverages a feature selection process to distill large volumes of heterogeneous data and predict specific equipment parameters. A variety of feature selection methods, including Shapley Additive Explanations (SHAP) and variance inflation factor (VIF), were used to select the optimal features as inputs for three ML methods: long short-term memory (LSTM) networks, support vector regression (SVR), and random forest (RF). Each combination of model and input features was used to predict a pump bearing temperature both 1 and 24 hours in advance, based on actual plant system data. The optimal inputs for the LSTM and SVR were selected using the SHAP values, while the optimal input for the RF consisted solely of the response variable itself. Each model produced similar 1-hour-ahead predictions, with root mean square errors (RMSEs) of roughly 0.006. For the 24-hour-ahead predictions, differences could be seen between LSTM, SVR, and RF, as reflected by model performances of 0.036 ± 0.014, 0.0026 ± 0, and 0.063 ± 0.004 RMSE, respectively. As big data and continuous online monitoring become more widely available, the proposed feature selection process can be used for many applications beyond the prediction of process parameters within nuclear infrastructure. This report summarizes the Fiscal Year 2021 research progress encompassing the (1) data cleaning and feature selection necessary for ML applications; (2) development of short-term forecasting models to predict future plant process parameters for both single and multiple time steps ahead; and (3) validation of the feature selection methods and short-term forecasting models given new data from different systems.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2021

Reclaimed Carbon Fiber Reinforced Automotive Part Using 3-DEP® Preforming Technology on Additive Manufacturing Tool Made with Reclaimed Carbon Fibers

The project focused on how well tools designed and built using reclaimed carbon fiber on the Big Area Additive Manufacturing (BAAM) can be used to manufacture preforms made with reclaimed carbon fibers. The project supported multiple Institute for Advanced Composite Manufacturing Innovation (IACMI) goals: • Enables the use of recycled carbon fibers in two areas: o Tooling material compounded with reclaimed carbon fiber. o Preforms made with recycled carbon fiber (rCF). • Lowers manufacturing costs by producing low cost 3-DEP® preform tooling. • Reduces manufacturing cycle time with quick additive manufacturing techniques. • Produces lightweight automotive components that will increase fuel economy which will in turn reduce greenhouse gas emissions. The project demonstrated a tool made from reclaimed carbon fibers, gathered technical data that will guide optimization of tooling materials, evaluated preforms made with the BAAM printed tool, and developed cost evaluations. This project has been a total success. First the project demonstrated that reclaimed carbon fiber can be compounded and successfully printed in the BAAM equipment. Second preforms were successfully made from reclaimed carbon fiber using a reclaimed fiber printed tool. Third the team successfully printed a molding tool out of reclaimed carbon fiber. Fourth the project went beyond the scope of this phase I project in 3 areas: (1)- Techmer PM compounded rCF in 2 resin systems instead of just one system (2)- Local Motors evaluated and collected data that will support their IACMI 3.6 project (Robert Bedsole, 2017) . (3)- University of Tennessee Knoxville (UTK) molded a part using the reclaimed preform on a molding tool made from printed reclaim fiber.

36 MATERIALS SCIENCE↗

DOE OSTI2022

Oppenheimer biographer tours Lab while movie is filmed locally

While a Hollywood movie crew filmed the Oppenheimer movie in Los Alamos these past few weeks, a special guest stopped by the Lab in between takes and chats with big-name actors. Director Thom Mason invited Kai Bird, co-author of American Prometheus: The Triumph and Tragedy of J. Robert Oppenheimer, to LANL while Bird was in town. The film’s director, Christopher Nolan, wrote the screenplay based on Bird’s Pulitzer Prize-winning biography, published in 2005. Oppenheimer was a physicist and the Lab’s first director of its wartime mission to secretly create the atomic bombs and help end World War II.

99 GENERAL AND MISCELLANEOUS↗

DOE OSTI2021

FOA 1861 Data Curation Overview

This document describes the process executed to collect, examine, and consolidate Phasor Measurement Unit (PMU) data from multiple transmission operators into a common dataset. The consolidated PMU data set was further anonymized and distributed to the Department of Energy Funding Opportunity Announcement (FOA) 1861 Big Data Analysis of Synchrophasor Data awardees.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗